Abstract: According to various embodiments, a machine-learning based system for coronavirus detection is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and questionnaire data from a user interface. The processors are further configured to train at least one neural network based on raw physiological data and questionnaire data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one coronavirus inference model. The processors are also configured to output a coronavirus-based decision by inputting the received physiological data and questionnaire data into the generated coronavirus inference model.

Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset is disclosed. The method includes providing an initial neural network architecture; performing a dataset modification on the dataset, the dataset modification including reducing dimensionality of the dataset; performing a first compression step on the initial neural network architecture that results in a compressed neural network architecture, the first compression step including reducing a number of neurons in one or more layers of the initial neural network architecture based on a feature compression ratio determined by the reduced dimensionality of the dataset; and performing a second compression step on the compressed neural network architecture, the second compression step including one or more of iteratively growing connections, growing neurons, and pruning connections until a desired neural network architecture has been generated.